By Ramya Shankar 18-22 minutes to read.

Sorry to say it again.

We all knew, “data is the new oil.”

Every organization preserving and securing their data. Even more, several organizations recruiting data scientists and data analysts to make their data effectively. And with the data, they create a long term impact on an organization. The decisions that are driven by the data too.

I’m passionate about the data. That’s why I was pursuing a data scientist course last January. So, I have to admit quite professionally and genuinely. I’m not much clear about the data collection methods and analysing the data patterns. I’m not from a coding background at all. But I have curiosity. I’m just working every day. I’m flexing my brain to learn a bit more.

I agree. It’s a long and tough journey. But it doesn’t mean, its impossible right!

Finally, not finally. Sorry, I need to attach an inspirational pic for us. And I need to conclude in a line. Then I will come to the content.

So, everywhere I see books to learn. I’m looking forward to learn a bit lot. Let’s seek constantly.

I will paste the source link down below. I sincerely encourage you all to visit further. Also,the author had added bonus books too. Totally 22 books. Let’s look at it. Start reading.

About the Author:

A cheerful, full of life and vibrant person, I hold a lot of dreams that I want to fulfill on my own. My passion for writing started with small diary entries and travel blogs, after which I have moved on to writing well-researched technical content. I find it fascinating to blend thoughts and research and shape them into something beautiful through my writing.

Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow.

That said, there is nothing better than reading data science books to get the ball rolling.

Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning and much more.

Data Science Books

Here are some of the best books that you can read to better understand the concepts of data science –

1. Head First Statistics: A Brain-Friendly Guide

Just like other books of Headfirst, the tone of this book is friendly and conversational. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. There are a lot of pictures and graphics and bits on the sides that are easy to remember. You can find some good real-life examples to keep you hooked on to the book. Overall a great book to begin your data science journey.

You can buy it here.

2. Practical Statistics for Data Scientists

If you are a beginner, this book will give you a good overview of all the concepts that you need to learn to master data science. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc… Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. The book also surprises one with a survey of ML models.

This book covers all the topics that are needed for data science. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.

You can buy it here.

3. Introduction to Probability

If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on.

This is perhaps the best book to learn about probability. The explanations are pretty neat and resemble real-life problems. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts. If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book.

The book has been one of the most popular books for about 5 decades and that is one more reason why it should definitely be on your bookshelf.

You can buy it here.

4. Introduction to Machine Learning with Python: A Guide for Data Scientists

This is a book that can get you kick-started on your ML journey with Python. The concepts are explained as if to a layman and with sufficient examples for a better understanding. The tone is friendly and easy to understand. ML is quite a complex topic, however, after practicing along with the book, you should be able to build your own ML models. You will get a good grasp of ML concepts. The book has examples in Python but you wouldn’t need any prior knowledge of either maths or Programming languages for reading this book.

This book is for beginners and covers basic topics in detail. However, reading this book alone won’t be sufficient as you get deeper into ML and coding.

You can buy it here.

5. Python Machine Learning By Example

 

As the name says, this book is the easiest way to get into machine learning. The book gets you started with Python and machine learning in a detailed and interesting way with some classy examples like the spam email detection using Bayes and predictions using regression and tree-based algorithms. The author shares his experiences in the various areas of ML such as ad optimization, conversion rate prediction, click fraud detection, etc. which beautifully adds to the reading experience.

Though the book covers the basics of Python, you might want to start the book after you gain some basic knowledge of Python. The book will help you through the process of setting up the required software until the creation, update, and monitoring of models. Overall, a great book for beginners as well as advanced users.

You can buy it here.

6. Pattern recognition and machine learning

This book is for all age groups, whether you are an undergraduate, graduate or advanced level researcher, there is something for everyone. If you have a Kindle subscription, this book will cost you nothing. Get the international edition that has colorful pictures and graphs making your reading experience totally worth it.

Coming to the content, this is one book that covers machine learning inside out. It is thorough and explains the concepts with examples in a simple way. Few readers could find some of the terms tough to understand but you should be able to get through using other free resources like web articles or videos. The book is a must-have if you are serious about getting into machine learning, especially the mathematical (data analytics) part is exhaustive in nature.

Though you can use the book for self-learning, it would be a better idea to read it alongside some machine learning courses.

You can buy it here.

7. Python for data analysis

True to its name, the book covers all the possible methods of data analysis. It is a great start for a beginner and covers basics about Python before moving on to Python’s role in data analysis and statistics. The book is fast-paced and explains everything in a super simple manner. You can build some real applications within a week of reading the book. This book can also give you a guideline or be a reference for the topics that you will be otherwise lost for when you search for online courses.

With focussed learning of both Python and data science, this book gives you a fair idea of what you can expect by being a data analyst or data scientist when you actually start working. The author also gives a lot of references in the book and points to useful resources that you will enjoy going through. Overall, a well-organized book with a thorough explanation of data analysis concepts.

You can buy it here.

8. Naked statistics

This book brings out the beauty of statistics and makes statistics come alive. The tone is witty and conversational. You will not get bored reading this book or feel the heaviness of math! The author explains all the concepts of statistics – basic and advanced with real-life examples. The book starts with very basic stuff like the normal distribution, central theorem and goes on to complex real-life problems and correlating data analysis and machine learning.

While the book explains the basics well, it will be good to have some prior knowledge of statistics with some of these courses, so that you can quickly get on with the book.

You can buy it here.

9. Data Science and big data analytics

This book gently introduces big data and how it is important in today’s digitally competitive world. The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system. The structure and flow of the book are very good and well organized. You can easily understand the entire big picture of how analytics is done as each step is like one chapter in the book. The book includes clustering, regression, association rules and much more along with simple, everyday examples that one can relate to. Advanced analytics using MapReduce, Hadoop, and SQL are also introduced to the reader.

If you are planning to learn data science with R, this is the book for you.

You can buy it here.

10. R for data science

Another book for beginners who want to learn data science using R. R with data science explains not just the concepts of statistics but also the kind of data you would see in real life, how to transform it using the concepts like median, average, standard deviation etc. and how to plot the data, filter and clean it. The book will help you understand how messy and raw real data is and how it is processed. Transformation of data is one of the most time-consuming tasks and this book will help you gain a lot of knowledge on different methods of transforming data for processing so that meaningful insights can be taken from it. If you want to learn R before you start with the book, you can do so with simple online courses, however, the book has enough basics covered so that you can start off right away.

You can buy it here.

Bonus Data Science Books

Here We are listing a few more good books which you might be interested in:

11. Inflection point

This is not a technical book. However, since you have decided to move into Data science career path, it will be necessary to know why data science and big data holds such an important place today. The book is written from a business perspective and offers a lot of insight into how all the technologies like cloud, big data, IT, mobility, infrastructure, and others are transforming the way businesses work today along with interesting stories and personal experiences to share. The changing times and how we should cope with it are described beautifully in this book.

It is a good read and will keep you motivated during your data science learning journey.

You can buy it here.

12. Storytelling with data

Anything told as a story and shown as graphics fit into our mind easily and stays there permanently. The book is quite impactful and deals with the fundamental concepts of data visualization for you to understand how to make the most of the huge chunks of data available in the real world. The author’s way of explaining every concept is totally unique as he tells it in the form of a compelling story. You wouldn’t even realize how many concepts you can grasp in a day of reading the book – getting to know the context and audience, using the right graph for the right situation, recognizing and removing the clutter to get only the important information, utilize the most significant parts of the data and present them to users – all of these and more.

You can buy it here.

13. Big Data – A revolution

This is a must-have book, a primer to your big data, data science, and AI journey. It is not a technical book but will give you the whole picture of how big data is captured, converted and processed into sales and profits even without users like us knowing about it. It explains how companies are using our data and the information that we share over the internet is used to create new business innovations and solutions that make our lives easier and connect all of us. It also talks about the risks and implications involved in doing so, and how security measures are placed to avoid breach or misuse of data. There are technical papers in the end that are quite helpful. A good, simple read for everyone.

You can buy it here.

14. Practical data science with R

 

 

This is a medium level book, a good balance of basic principles and advanced data science principles. The keen focus is on business demands which is what makes the book very practical and interesting. It also explains statistics thoroughly which is one of the foundations of data science. Most books just explain how things are done – this book explains how and why! That helps motivate the readers to get into deep learning and machine learning. This is a good book for beginners and advanced level data scientists alike. It gets tougher as the advance of the topic but you can follow most of the book easily.

You can buy it here.

15. The data science handbook

This is an advanced book. If you have a little knowledge about statistics and data science through other books or tutorials, you will be able to appreciate the content of the book. It is not a purely technical book but a quick reference as it contains information in the form of questions and answers from various leading data scientists. The questions flow in an organized manner and help you understand each aspect of data science like data preparation, the importance of big data, the process of automation and how data science is the future of the digital world. The book lacks real case-studies though, however, if you have a business mindset, you will get to know a lot of strategies and tips from renowned data scientists who have been there, done that.

You can buy it here.

16. Business analytics – the science of data-driven decision making

This is an awesome in-depth book that explains the theory as well as practical applications to give wholesome knowledge. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow. The book has everything from economics, statistics, finance and all you need to start learning data science. The book has been written with a lot of effort and experience and the way insights have been presented shows the same. It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. You will also learn about scholastic models and six sigma towards the end of the book.

You can buy it here.

17. Data mining techniques

A wonderful book that explains data mining from scratch. So much so, that you need not be a computer science graduate to understand this book. It starts with explaining about the digital age, data mining and then moves to explain the kinds of data that can be mined, the patterns that can be mined, for example, cluster analysis, predictive analysis, correlations, etc., and the technologies that are used – statistics, machine learning, and database. The book is purely technical and you can go step-by-step to fully enjoy the book. The book is detailed – a must-have on your collection.

It has a lot of basic and advanced techniques for classification, cluster analysis and also talks about the trends and on-going research in the field of data mining.

You can buy it here.

18. Thinking with data

This is a small book that can be read along with other reading materials and online courses. It provides a lot of useful insights and enables critical business thinking in the reader. It helps you relate to why things are happening the way they are. Through the chapters, you will learn how to ask good meaningful questions, note down the important details of an idea and get key information to focus on. It nicely covers data-specific patterns of reasoning. The book will help you think ‘why’ and not just ‘how’. It covers what is called as CoNVO – context, needs, vision, and outcome.

You can buy it here.

19. Machine learning with PySpark

The book covers in detail about machine learning models, NLP (Natural language processing) applications and recommender systems using PySpark. It helps you understand the real-world business challenges and solve them. It covers linear regression, decision tree, logistic regression, and other supervised learning techniques. This book will enrich your knowledge greatly especially if you don’t just read it, rather work with the book and practice. You will also be able to appreciate the rich libraries of PySpark that are ideal for machine learning and data analysis. A great book to learn recommender systems using Spark – neat and simple.

You can buy it here.

20. Generative Deep learning

The book is like any other fiction book that keeps you hooked up till the last page. If you have read Harry Potter, you will know what we are talking about. The author has done an exceptional job in penning all the concepts in the form of stories that are easy to comprehend. The subjects of statistics and intuitive learning are a bit dry otherwise and this book does its best to make it as interactive and interesting as possible. If you read other books, you will realize how complex neural networks and probability are. This book makes it simple. Before starting the book, familiarise yourself with Python through some courses or tutorials. One of the best books for deep learning techniques from scratch.

You can buy it here.

21. Data Science for business

Purely business-oriented, this is one book to start with if you are not able to make up your mind into the field of data science. It clearly explains why you should learn data science and why it is the right choice for you. There are beautiful examples like the recommendation system, telecom churn rate, automated stock market analysis and more. The book keeps you motivated. It is not a book that will preach though. It is practical and gives you enough references to start with your technical journey too. The book emphasizes on discovering new business cases rather than just processing and analyzing data.

Check out a preview of the book on Amazon to know the concepts that are taken up in the book.

You can buy it here.

22. Designing data-intensive applications

Last, but not least, this book helps understand the architecture of today’s data systems and how they can be fit into applications that are data-driven and data-intensive. It doesn’t go into depth on management, security, installation and other things but explains data retrieval, database systems and fundamental concepts at length. This book is for you if you are an architect. The author discusses various aspects of designing database and data solutions and gives loads of other resources too (at the end of every chapter!) for you to further your knowledge on the topic.

You can buy it here.

More to go….

There are hundreds or more books related to data analytics and data science and don’t be overwhelmed with the huge chunk of books. You don’t have to read them all. We have carefully selected these and you should be able to build real-world models and get in-depth knowledge of data science with these books and the other resources mentioned in the blog. A few more reference books that can be helpful are Teach yourself SQL, too big to ignore, the hundred-page machine learning book, communicating data with Tableau and data analytics made accessible. Start your data science journey with any of the 22 books we have suggested and let us know how you liked reading them!

 

SOURCES: https://hackr.io/blog/data-science-books

 

With respect.

 

 

 

 

Yesterday, I felt bad. I need to attach the picture of all the three. AI, ML, and DP. I missed it. I forget it. Sorry. Also, I would like to the AI books that I shared in last December. Please have a look and start reading it.

https://writerscommunity.home.blog/2019/12/11/15-best-books-on-ai/

 

Alright, today Deep Learning also comes. Because, as I said yesterday. We cannot avoid it. What I would say is, we must look forward to learn. I personally had a passion to learn. Although, I’m not a tech-savvy at all. I don’t feel bad. That’s okay.

Things are gonna be hard in the beginning.

I was totally struggled to find books for Deep Learning. Anyway, I got a very good books. So, have a look and start reading one by one.

About the Author:

Hi there, I’m Adrian Rosebrock, PhD. All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. I created this website to show you what I believe is the best possible way to get your start.

I gonna share the source down below. I sincerely encourage you all to visit further.

In today’s post I’m going to share with you the 7 best deep learning books (in no particular order) I have come across and would personally recommend you read.

Some of these deep learning books are heavily theoretical, focusing on the mathematics and associated assumptions behind neural networks and deep learning.

Other deep learning books are entirely practical and teach through code rather than theory.

And even other deep learning books straddle the line, giving you a healthy dose of theory while enabling you to “get your hands dirty” and learn by implementing (these tend to be my favorite deep learning books).

For each deep learning book I’ll discuss the core concepts covered, the target audience, and if the book is appropriate for you.

To discover the 7 best books for studying deep learning, just keep reading!

The 7 best deep learning books you should be reading right now

Before you choose a deep learning book, it’s best to assess your own personal learning style to ensure you get the most out of the book.

Start by asking yourself the following question:

How do I best learn? Do I like to learn from theoretical texts? Or do I like to learn from code snippets and implementation?

Everyone has their own personal learning style and your answers here will dictate which deep learning books you should be reading.

For me personally, I like to strike a balance between the two.

Deep learning books that are entirely theoretical and go too far into the abstract make it far too easy for my eyes to gloss over.

But on the other hand, if a deep learning book skips theory entirely and jumps straight into implementation, I know I’m missing out on core theoretical underpinnings that may help me approach a new deep learning problem or project.

In my opinion, a good deep learning book needs to carefully balance the two.

We need theory to help us understand the core fundamentals of deep learning — and at the same time we need implementation and code snippets to help us reinforce what we just learned.

1. Deep Learning

It’s hard (if not impossible) to write a blog post regarding the best deep learning books without mentioning Goodfellow, Bengio, and Courville’s Deep Learning text.

This book is meant to be a textbook used to teach the fundamentals and theory surrounding deep learning in a college-level classroom.

Goodfellow et al.’s Deep Learning is entirely theoretical and written for an academic audience. There is no code covered in the book.

The book starts with a discussion on machine learning basics, including the applied mathematics needed to effectively study deep learning (linear algebra, probability and information theory, etc.) from an academic perspective.

From there, the book moves into modern deep learning algorithms and techniques.

The final part of Deep Learning focuses more on current research trends and where the deep learning field is moving.

I’ve personally read through this book twice, cover to cover, and have found it incredibly valuable, provided you have the mathematical/academic rigor required for such a textbook.

Deep Learning is available for online viewing for free from the book’s homepage. You can purchase a hardcopy of the text from Amazon.

You should read this deep learning book if…

• You learn from theory rather than implementation
• You enjoy academic writing
• You are a professor, undergraduate, or graduate student doing work in deep learning

2. Neural Networks and Deep Learning

My second theory-based deep learning (e)book recommendation is Neural Networks and Deep Learning by Michael Nielsen.

The book does include some code but it’s important to underline the “some” — there are a total of seven Python scripts accompanying the book, all discussing a various fundamental machine learning, neural network, or deep learning technique on the MNIST dataset. The implementations are not the most “exciting” in the world, but they will help demonstrate some of the theoretical concepts in the text.

If you are new to machine learning and deep learning but are eager to dive into a theory-based learning approach, Nielsen’s book should be your first stop.

The book is a much quicker read than Goodfellow’s Deep Learning and Nielsen’s writing style combined with occasional code snippets makes it easier to work through.

You should read this deep learning book if…

• You are looking for a theory-based deep learning text
• Are new to machine learning/deep learning and want to approach the field from a more academic standpoint

3. Deep Learning with Python

Francois Chollet, Google AI researcher and creator of the popular Keras deep learning library, published his book, Deep Learning with Python in October 2017.

Francois’ book takes a practitioner’s approach to deep learning. Some theory and discussion is included, but for every few paragraphs of theory, you’ll find a Keras implementation of the technique.

One of my favorite aspects of this book is how Francois includes examples for deep learning applied to computer vision, text, and sequences, making it a well rounded book for readers who want to learn the Keras library while studying machine learning and deep learning fundamentals.

I found Francois’ writing to be clear and accessible. His additional commentary on deep learning trends and history is phenomenal and insightful.

It’s important to note that this book is not meant to be a super deep dive into deep learning. Instead, it’s primary use is to teach you (1) the fundamentals of deep learning (2) through the Keras library (3) using practical examples in a variety of deep learning domains.

You should read this deep learning book if…

• You are interested in the Keras library
• You “learn by doing/implementing”
• You want a quick understanding of how deep learning is applied to various fields, such as computer vision, sequence learning, and text

4. Hands-On Machine Learning with Scikit-Learn and TensorFlow

When I first purchased a copy of Aurélien Géron’s Hands-on Machine Learning with Scikit-Learn and TensorFlow, I wasn’t sure what to expect — had the title not included the word “TensorFlow” I may have breezed right by it, thinking it was only a basic introduction to machine learning.

But at the same time, appending the word “TensorFlow” to an already lengthy title that seems to focus on basic machine learning made me think it was a cheap marketing tactic to sell more copies — everyone is interested in deep learning, right?

Luckily, I was wrong — the book is a good read and the title shouldn’t deter you from reading through it.

Géron’s deep learning book is organized in two parts.

The first part covers basic machine learning algorithms such as Support Vector Machines (SVMs), Decision, Trees, Random Forests, ensemble methods, and basic unsupervised learning algorithms. Scikit-learn examples for each of the algorithms are included.

The second part then covers elementary deep learning concepts through the TensorFlow library.

You should read this deep learning book if…

• You are new to machine learning and want to start with core principles with code examples
• You are interested in the popular scikit-learn machine learning library
• You want to quickly learn how to operate the TensorFlow library for basic deep learning tasks

5. TensorFlow Deep Learning Cookbook

If you like the “cookbook” style of teaching (little-to-no theory and lots of code), I would suggest taking a look at Gulli and Kapoor’s TensorFlow Deep Learning Cookbook.

This deep learning book is entirely hands-on and is a great reference for TensorFlow users.

Again, this book is not meant to necessarily teach deep learning, but instead show you how to operate the TensorFlow library in the context of deep learning.

Don’t get me wrong — you will absolutely learn new deep learning concepts, techniques, and algorithms along the way, but the book takes a heavy-handed cookbook approach: lots of code and explanations of what the code is doing.

My only criticism of the book is that there are some typos in the code snippets. This can be expected when writing a book that is entirely code focused. Typos happen, I can certainly attest to that. Just be aware of this when you are working through the text.

You should read this deep learning book if…

• You have already studied the fundamentals of deep learning
• You are interested in the TensorFlow library
• You enjoy the “cookbook” style of teaching where code is provided to solve a particular problem but the underlying theory is not discussed

6. Deep Learning: A Practitioners Approach

While most deep learning books that include code samples use Python, Adam Gibson and Josh Patterson’s Deep Learning: A Practitioners Approach instead use Java and the DL4J library.

Why Java?

Java is the most used programming language in large corporations, especially at the enterprise level.

The first few chapters in Gibson and Patterson’s book discuss basic machine learning and deep learning fundamentals. The rest of the book includes Java-based deep learning code examples using DL4J.

You should read this deep learning book if…

• You have a specific use case where the Java programming language needs to be utilized
• You work for a large company or enterprise organization where Java is primarily used
• You want to understand how to operate the DL4J library

7. Deep Learning for Computer Vision with Python

I’ll be completely honest and forthcoming and admit that I’m biased — I wrote Deep Learning for Computer Vision with Python.

That said, my book really has become one of the best deep learning and computer vision resources available today (take a look at this review and this one as well if you need an honest second opinion).

Francois Chollet, AI researcher at Google and creator of Keras, had this to say about my new deep learning book:

This book is a great, in-depth dive into practical deep learning for computer vision. I found it to be an approachable and enjoyable read: explanations are clear and highly detailed. You’ll find many practical tips and recommendations that are rarely included in other books or in university courses. I highly recommend it, both to practitioners and beginners. — Francois Chollet

And Adam Geitgey, the author of the popular Machine Learning is Fun! blog series, said this:

I highly recommend grabbing a copy of Deep Learning for Computer Vision with Python. It goes into a lot of detail and has tons of detailed examples. It’s the only book I’ve seen so far that covers both how things work and how to actually use them in the real world to solve difficult problems. Check it out! — Adam Geitgey

If you’re interested in studying deep learning applied to computer vision (image classification, object detection, image understanding, etc.), this is the perfect book for you.

Inside my book you will:

• Learn the foundations of machine learning and deep learning in an accessible manner that balances both theory and implementation
• Study advanced deep learning techniques, including object detection, multi-GPU training, transfer learning, and Generative Adversarial Networks (GANs)
• Replicate the results of state-of-the-art papers, including ResNet, SqueezeNet, VGGNet, and others on the 1.2 million ImageNet dataset

Furthermore, I provide the best possible balance of both theory and hands-on implementation. For each theoretical deep learning concept you’ll find an associated Python implementation to help you cement the knowledge.

Be sure to take a look — and while you’re checking out the book, don’t forget to grab your (free) table of contents + sample chapters PDF of the book.

You should read this deep learning book if…

• You are specifically interested in deep learning applied to computer vision and image understanding
• You want an excellent balance between theory and implementation
• You want a deep learning book that makes seemingly complicated algorithms and techniques easy to grasp and understand
• You want a clear, easy to follow book to guide you on your path to deep learning mastery

Summary

In this post you discovered my seven favorite books for studying deep learning.

Have you purchased or read through one of these books? If so, leave a comment and let me know what you think of it.

Did I miss a book that you think should be on this list? If so, be sure to contact me or leave a comment.

 

SOURCE: https://www.pyimagesearch.com/2018/03/05/7-best-deep-learning-books-reading-right-now/

 

With respect.

 

Last December, I re-shared a post 15 books for AI. So far, I started learning a bit more about AI. Precisely, I started searching documentaries, books and experts views on AI. When I started watching/learning it. The term Machine Learning arises more often. At some point, I saw the relationship between Artificial Intelligence, Machine Learning and Deep Learning. More often, I had seen the pic (like a circle).

Then over the last few months, I started searching and researching more and more. Finally, I came to know, if you wanna learn AI, simultaneously you should learn Machine Learning and Deep Learning too.

Where and how to start?

Books came to my mind. So, I searched a bit more.

Here comes, 7 books by Tableau.

Machine learning and artificial intelligence are growing fields and growing topics of study. While the advanced implementations of machine learning we hear about in the news might sound scary and inaccessible, the core concepts are actually pretty easy to grasp. In this article, we’ll review some of the most popular resources for machine learning beginners (or anyone just curious to learn). Some of these books will require familiarity with some coding languages and math, but we’ll be sure to mention it when that’s the case.

1. “Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)” by Oliver Theobald

Author: Oliver Theobald
Website: Amazon

The title is kind of explanatory, right? If you want the complete introduction to machine learning for beginners, this might be a good place to start. When Theobald says “absolute beginners,” he absolutely means it. No mathematical background is needed, nor coding experience — this is the most basic introduction to the topic for anyone interested in machine learning.

“Plain” language is highly valued here to prevent beginners from being overwhelmed by technical jargon. Clear, accessible explanations and visual examples accompany the various algorithms to make sure things are easy to follow. Some simple programming is also introduced to put machine learning in context.

2. “Machine Learning For Dummies” by John Paul Mueller and Luca Massaron

Authors: John Paul Mueller and Luca Massaron
Website: Amazon

While we’re going with “absolute beginners,” the popular “Dummies” series is another useful starting point. This book aims to get readers familiar with the basic concepts and theories of machine learning and how it applies to the real world. It presents the programming languages and tools integral to machine learning and illustrates how to turn seemingly-esoteric machine learning into something practical.

The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. From those small tasks and patterns, we can extrapolate how machine learning is useful in daily lives through web searches, internet ads, email filters, fraud detection, and so on. With this book, you can take a small step into the realm of machine learning.

3. “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies” by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

Authors: John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy
Website: Amazon

This book covers all the fundamentals of machine learning, diving into the theory of the subject and using practical applications, working examples, and case studies to drive the knowledge home. “Fundamentals” is best read by people with some analytics knowledge.

It presents the different learning approaches with machine learning and accompanies each learning concept with algorithms and models, along with working examples to show the concepts in practice.

4. “Programming Collective Intelligence” by Toby Segaran

Author: Toby Segaran
Website: O’Reilly | Amazon

This is more of a practical field guide for implementing machine learning rather than an introduction to machine learning. In this book, you’ll learn about how to create algorithms in machine learning to gather data useful to specific projects. It teaches readers how to create programs to access data from websites, collect data from applications, and figure out what that data means once you’ve collected it.

“Programming Collective Intelligence” also showcases filtering techniques, methods to detect groups or patterns, search engine algorithms, ways to make predictions, and more. Each chapter includes exercises to display the lessons in application.

5. “Machine Learning for Hackers” by Drew Conway and John Myles White

Authors: Drew Conway and John Myles White
Website: O’Reilly | Amazon

Here, the word ‘hackers’ is used in the more technical sense: programmers who hack together code for specific goals and practical projects. For those who aren’t well versed in the mathematics, but are experienced with programming and coding languages, “Machine Learning for Hackers” comes in. Machine learning is usually based on a lot of math, due to the algorithms needed for it to parse data, but a lot of experienced coders don’t always develop those math skills.

The book uses hands-on case studies to present the material in real-world practical applications rather than going heavy on mathematical theory. It presents typical problems in machine learning and how to solve them with the R programming language. From comparing U.S. Senators based on their voting records to building a recommendation system for who to follow on Twitter, to detecting spam emails based on the email text, machine learning applications are endless.

6. “Machine Learning in Action” by Peter Harrington

Author: Peter Harrington
Website: Amazon

“Machine Learning in Action” is a guide to walk newcomers through the techniques needed for machine learning as well as the concepts behind the practices. It acts as a tutorial to teach developers how to code their own programs to acquire data for analysis.

In this book you’ll learn the techniques used in practice with a strong focus on the algorithms themselves. The programming language snippets feature code and algorithm examples to get you started and see how it advances machine learning. Familiarity with Python programming language is helpful since it is used in most of the examples.

7. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall

Authors: Ian H. Witten, Eibe Frank, and Mark A. Hall
Website: Amazon

In “Data Mining,” the authors focus on the technical work in machine learning and how to gather the data you need from specific mining techniques. They go into the technical details for machine learning, teaching the methods to obtain data, as well as how to use different inputs and outputs to evaluate results.

Because machine learning is ever-changing, the book also discusses modernization and new software that shape the field. Traditional techniques are also presented alongside new research and tools. Of particular note is the authors’ own software, Weka, developed for applied machine learning.

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SOURCE: https://www.tableau.com/learn/articles/books-about-machine-learning

 

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